A Machine Learning Web Application that predicts the price of used cars based on important features like car name, company, year, fuel type, and kilometers driven.
This project helps users estimate the fair market value of a used car quickly and accurately.
This project uses Machine Learning to understand how different factors affect the price of a car.
The model is trained on real-world car data collected from the Quikr Cars dataset.
It performs data cleaning, feature engineering, and model training using algorithms like Linear Regression and XGBoost to provide accurate price predictions.
The web app is built with Flask, and has an easy-to-use interface where users can input details and get instant predictions.
- Predicts car price based on user input
- User-friendly web interface
- Preprocessed and cleaned dataset
- Machine Learning model trained using Python
- Integrated with Flask backend for live predictions
Here’s a preview of the prediction form:
car_price_predictor/ │ ├── model/ │ ├── Car_Price_Predictor.ipynb # Jupyter Notebook (model training) │ ├── quikr_car.csv # Raw dataset │ ├── Cleaned_data.csv # Cleaned dataset │ ├── model.joblib # Trained model file │ ├── le.joblib # Label Encoder │ ├── ohe.joblib # One Hot Encoder │ └── scaler.joblib # Scaler for numerical columns │ ├── templates/ │ └── index.html # Frontend HTML form │ ├── app.py # Flask backend ├── requirements.txt # Required libraries └── README.md # Project documentation
- Clone this repository:
git clone https://github.com/aliahmad552/car_price_predictor.git
Navigate to the project directory:
cd car_price_predictor
Create and activate a virtual environment (optional but recommended):
python -m venv myenv myenv\Scripts\activate
Install the required dependencies:
pip install -r requirements.txt
Run the Flask app:
python app.py
Open your browser and visit:
The user enters car details like:
Company name
Car model
Year of purchase
Fuel type
Kilometers driven
The model processes the input data and predicts the estimated selling price of the car.
Python
Flask
Pandas, NumPy, Scikit-learn
Joblib (for model saving/loading)
HTML, CSS (for frontend)
The trained model provides high accuracy for car price predictions and performs well on unseen data. The web app makes it simple and interactive to get price estimates instantly.
Author: Ali Ahmad GitHub: aliahmad552
Email: aliahmaddawana@gmail.com